Manage MLOps workflow stages and connections.
Technology management has moved! You can now add and edit technologies directly on the Tools page with convenient edit buttons on each tool card.
Keep track of important information about your experiments such as parameters, metrics and models.
ID: experiment_tracking | Position: (50, 50)
Explore your data and run scripts interactively. Have your code, text, data and visualizations in a single place.
ID: experimentation | Position: (350, 50)
Capture versions of your data to reproduce, trace, and keep track of your ML model lineage.
ID: data_versioning | Position: (50, 220)
Version your notebooks, pipelines and configuration files.
ID: code_versioning | Position: (350, 220)
Automate the steps of your ML experiments. Schedule automatic pipeline runs to retrain the model on new data.
ID: pipeline_orchestration | Position: (350, 390)
Store your models in a centralized repository to track and deploy them.
ID: model_registry | Position: (650, 390)
Create API endpoints and use your model to make predictions.
ID: model_serving | Position: (950, 390)
Store and keep track of datasets, models, and evaluation across your experiments and pipelines.
ID: artifact_tracking | Position: (350, 560)
Track your model to detect performance degradation, bias and data drift. Detect issues early and take action.
ID: model_monitoring | Position: (950, 560)
Optimize your code and distribute execution across multiple machines to improve performance.
ID: runtime_engine | Position: (50, 730)
No custom connections added yet.